1. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics Forecasting Day-ahead Electricity Prices and Regulation Costs in Markets With Significant Wind Power Penetration March 19th 2009 EWEC 2009 – Marseille Tryggvi Jónsson ENFOR A/S & DTU Informatics Pierre Pinson DTU Informatics Henrik Madsen DTU Informatics March 19th 2009 EWEC 2009, Marseille

2. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics Intro The nature of wind power places it inside the equilibrium at almost all times. Wind power enters the day-ahead electricity supply function as a stochastic quantity with uncertainty attached to it. The most rapid changes in the supply function are mostly owed to wind power. Case study: DK-1 area of Nord Pool’s Elspot Nord Pool hydro dominated – DK-1 heavily penetrated by wind March 19th 2009 EWEC 2009, Marseille

3. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics On the market power of wind energy (forecasts) - I March 19th 2009 EWEC 2009, Marseille

4. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics On the market power of wind energy (forecasts) – II March 19th 2009 EWEC 2009, Marseille

5. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics Model for forecasting day-ahead prices Propose a three layer approach to forecast the day-ahead price consisting of: i. A mapping of forecasted wind power penetration and time to the average prices ii. Dynamical weighting of recently observed prices and prediction error together with the output from the first layer. iii. Uncertainty estimation conditioned upon the forecasted wind power penetration and the forecasted mean price New observations considered as soon as they become available Older observations discounted either exponentially or by a rolling window March 19th 2009 EWEC 2009, Marseille

6. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics Forecasting properties Root Mean Square ErrorForecasts on January 12th 2007 March 19th 2009 EWEC 2009, Marseille

7. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics Forecasting regulation costs Imbalances on the producers side are priced by a two price model at Elspot Implies no regulation costs for producers bringing the system back to balance Forecasting of regulation costs is done in two steps i. Prediction of the sign of the system imbalance ii. Probabilistic forecasting of the penalty March 19th 2009 EWEC 2009, Marseille Down regulation hoursUp regulation hours Cost Imbalance

8. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics Predicting the system’s imbalance sign Predict the imbalance sign by binary classification for each “direction” Do not believe in linearly separable non-overlapping classes Explanatory variables are i. Forecasted wind power penetration ii. Spot price forecast iii. Time variables iv. Import/Export forecasts March 19th 2009 EWEC 2009, Marseille Down regulationUp regulation Hit rate75.5%76.4% Bias1.17%-0.20%

9. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics Predicting the imbalance penalties Predict price intervals with certain probabilities directly Predictions conditioned upon the forecasted sign March 19th 2009 EWEC 2009, Marseille Down regulationUp regulation

10. ENFOR Tryggvi Jónsson, ENFOR A/S & DTU Informatics Summary & Final Remarks The impact of wind power forecasts on the day-ahead prices is substantial and nonlinear Same applies for regulation costs Wind power forecasts therefore play an important role in price forecasting More intelligent trading with benefits for both producers and the system as a whole More efficient risk hedging as well Methods are operationally available and results indicate that they can be tailored to other markets March 19th 2009 EWEC 2009, Marseille